Zobrazeno 1 - 10
of 442 386
pro vyhledávání: '"Bryan, A"'
Autor:
Wang, Jade, Bilyeu, Bryan, Boroson, Don, Caplan, Dave, Riesing, Kat, Robinson, Bryan, Schieler, Curt, Johnson, Michael D., Blackburn, Lindy, Haworth, Kari, Houston, Janice, Issaoun, Sara, Palumbo, Daniel, Richards, Elliot, Srinivasan, Ranjani, Weintroub, Jonathan, Marrone, Dan
The Black Hole Explorer (BHEX) is a mission concept that can dramatically improve state-of-the-art astronomical very long baseline interferometry (VLBI) imaging resolution by extending baseline distances to space. To support these scientific goals, a
Externí odkaz:
http://arxiv.org/abs/2406.09572
This paper presents a mapping strategy for interacting with the latent spaces of generative AI models. Our approach involves using unsupervised feature learning to encode a human control space and mapping it to an audio synthesis model's latent space
Externí odkaz:
http://arxiv.org/abs/2407.04379
Autor:
Lau, Gregory Kang Ruey, Niu, Xinyuan, Dao, Hieu, Chen, Jiangwei, Foo, Chuan-Sheng, Low, Bryan Kian Hsiang
Protecting intellectual property (IP) of text such as articles and code is increasingly important, especially as sophisticated attacks become possible, such as paraphrasing by large language models (LLMs) or even unauthorized training of LLMs on copy
Externí odkaz:
http://arxiv.org/abs/2407.04411
Graph pre-training has been concentrated on graph-level on small graphs (e.g., molecular graphs) or learning node representations on a fixed graph. Extending graph pre-trained models to web-scale graphs with billions of nodes in industrial scenarios,
Externí odkaz:
http://arxiv.org/abs/2407.03953
Learning from examples of success is an appealing approach to reinforcement learning that eliminates many of the disadvantages of using hand-crafted reward functions or full expert-demonstration trajectories, both of which can be difficult to acquire
Externí odkaz:
http://arxiv.org/abs/2407.03311
Autor:
Ji, Ziwei, Chen, Delong, Ishii, Etsuko, Cahyawijaya, Samuel, Bang, Yejin, Wilie, Bryan, Fung, Pascale
The hallucination problem of Large Language Models (LLMs) significantly limits their reliability and trustworthiness. Humans have a self-awareness process that allows us to recognize what we don't know when faced with queries. Inspired by this, our p
Externí odkaz:
http://arxiv.org/abs/2407.03282
Autor:
Bowden, Jack, Madsen, Jesper, Goldman, Bryan, Iversen, Aske Thorn, Liang, Xiaoran, Vansteelandt, Stijn
The STEP 1 randomized trial evaluated the effect of taking semaglutide vs placebo on body weight over a 68 week duration. As with any study evaluating an intervention delivered over a sustained period, non-adherence was observed. This was addressed i
Externí odkaz:
http://arxiv.org/abs/2407.02902
Distribution shift is a key challenge for predictive models in practice, creating the need to identify potentially harmful shifts in advance of deployment. Existing work typically defines these worst-case shifts as ones that most degrade the individu
Externí odkaz:
http://arxiv.org/abs/2407.03557
Autor:
Yu, Yue, Ping, Wei, Liu, Zihan, Wang, Boxin, You, Jiaxuan, Zhang, Chao, Shoeybi, Mohammad, Catanzaro, Bryan
Large language models (LLMs) typically utilize the top-k contexts from a retriever in retrieval-augmented generation (RAG). In this work, we propose a novel instruction fine-tuning framework RankRAG, which instruction-tunes a single LLM for the dual
Externí odkaz:
http://arxiv.org/abs/2407.02485
Autor:
Nguyen, Maximilian, Freedman, Ari, Cheung, Matthew, Saad-Roy, Chadi, Espinoza, Baltazar, Grenfell, Bryan, Levin, Simon
Risk-driven behavior provides a feedback mechanism through which individuals both shape and are collectively affected by an epidemic. We introduce a general and flexible compartmental model to study the effect of heterogeneity in the population with
Externí odkaz:
http://arxiv.org/abs/2407.03376